<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tool Use on XiDao Tech Blog</title><link>https://blog.xidao.online/en/tags/tool-use/</link><description>Recent content in Tool Use on XiDao Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 XiDao</copyright><lastBuildDate>Mon, 11 May 2026 09:00:00 +0800</lastBuildDate><atom:link href="https://blog.xidao.online/en/tags/tool-use/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Structured Output and Function Calling in 2026: A Complete Guide from JSON Mode to Tool Use</title><link>https://blog.xidao.online/en/posts/2026-llm-structured-output-function-calling/</link><pubDate>Mon, 11 May 2026 09:00:00 +0800</pubDate><guid>https://blog.xidao.online/en/posts/2026-llm-structured-output-function-calling/</guid><description>&lt;h2 class="relative group"&gt;Introduction: Why Structured Output Matters
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&lt;p&gt;In 2026, large language model (LLM) applications have evolved from simple chatbots to complex autonomous agent systems. Throughout this evolution, one fundamental technical challenge has persisted: &lt;strong&gt;how to make LLM outputs reliably parseable by programs.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;Traditional LLM output is free-form text, forcing developers to use fragile approaches like regex and string matching to extract information. Structured Output and Function Calling (Tool Use) technologies have completely changed this paradigm.&lt;/p&gt;</description></item></channel></rss>